Stanford AI Team Apologizes for Copying China's Tsinghua University Model

A team of artificial intelligence researchers from Stanford University has issued a public apology for plagiarizing an open-source large language model developed by Tsinghua University and ModelBest Inc. The incident has attracted significant attention, particularly on Chinese social media platforms.

This incident highlights the importance of academic integrity and proper citation in AI research, which is essential for the development of trustworthy and reliable AI systems. Failure to uphold these standards can lead to the proliferation of flawed or biased models, with potential consequences for industries and individuals that rely on them.

The Stanford team, consisting of Siddharth Sharma, Aksh Garg, and Mustafa Aljadery, admitted to copying the miniCPM-Llama3-V 2.5 model without proper verification or attribution. The model, developed by Tsinghua University and ModelBest, includes unique features, specifically the ability to recognize ancient Chinese texts, specifically the Tsinghua Bamboo Slips.

In their apology posted on social media platform X, Sharma and Garg expressed regret for their oversight. 'We sincerely apologize to the authors of MiniCPM for our failure to verify the originality of Llama3-V... We take full responsibility for this oversight,' they stated. The team has since withdrawn the Llama3-V model and removed all references to it.

The plagiarism was discovered when Chinese internet users noticed the striking similarities between the Llama3-V model and the miniCPM model. The Stanford model not only replicated the structure and code but also the unique errors related to the recognition of the Tsinghua Bamboo Slips.

Liu Zhiyuan, co-founder of ModelBest, expressed disappointment over the incident but also acknowledged the global recognition of their model. 'Even the wrong cases are the same,' Liu remarked, highlighting the extent of the copying.

Christopher Manning, director of the Stanford AI Lab, distanced the institution from the controversy, stating, 'This appears to be the work of a few individuals, not a reflection of our collective ethos.' Manning also criticized the 'fake it till you make it' approach prevalent in Silicon Valley.

The incident highlights the importance of academic integrity and proper citation in AI research. As AI continues to evolve, the need for ethical standards and respect for intellectual property becomes increasingly vital. The rapid development of AI technologies relies heavily on global open-source sharing, making adherence to open-source protocols crucial.

In the wake of the controversy, the Stanford team has pledged to be more cautious and diligent in their future work. The broader AI community continues to watch closely, emphasizing the need for transparency and collaboration in advancing the field.

Key Takeaways

• Stanford AI researchers plagiarized a Chinese open-source language model, sparking controversy.
• The copied model, miniCPM-Llama3-V 2.5, included unique features for ancient Chinese texts.
• The researchers apologized, withdrew the model, and removed references to it.
• The incident highlights the importance of academic integrity and proper citation in AI research.
• The AI community emphasizes the need for transparency and collaboration in advancing the field.